Start with one task
Choose a task your team repeats, such as checking an application or preparing a renewal message. Walk through one recent example with the person responsible. Record where the information came from, who handled it and where work stopped.
Agree what the status means
“Complete” is not a universal definition. An application may need a particular set of documents, a duplicate check and an owner’s review before it is ready for the next stage. Write down those conditions before asking AI to interpret the record.
Separate suggestions from actions
Extracting a proposed value is different from changing a customer record. Drafting a message is different from sending it. Name the actions the system may take and the points where a person must review the result.
Define the exception path
Use examples with a missing document, conflicting information and an unavailable connection. Agree who receives each exception and what the system should do while waiting. A visible review queue is more useful than a silent failure.
Make the first test useful
Build a reviewed set of examples before testing. Compare the system’s output with the expected result, including cases where it should ask for help. Measure the work left for the reviewer as well as the time taken.
Bring five answers to the first conversation
What should improve? Which records does the task use? What do the key statuses mean? Which actions are permitted? Who reviews exceptions? Those answers form a practical starting point for a scoped AI project.